It contains basic versions of many of the most common optimization algorithms that do not require the calculation of gradients, and allows for very rapid development using them.
It's a very versatile library that's great for learning, modifying, and of course, using out-of-the-box.
See the detailed documentation here.
- Genetic Algorithm
- Evolutionary Algorithm
- Simulated Annealing
- Particle Swarm Optimization
- Tabu Search
- Harmony Search
- Stochastic Hill Climb
pip install solidpy- Import the relevant algorithm
- Create a class that inherits from that algorithm, and that implements the necessary abstract methods
- Call its
.run()method, which always returns the best solution and its objective function value
from random import choice, randint, random
from string import lowercase
from Solid.EvolutionaryAlgorithm import EvolutionaryAlgorithm
class Algorithm(EvolutionaryAlgorithm):
"""
Tries to get a randomly-generated string to match string "clout"
"""
def _initial_population(self):
return list(''.join([choice(lowercase) for _ in range(5)]) for _ in range(50))
def _fitness(self, member):
return float(sum(member[i] == "clout"[i] for i in range(5)))
def _crossover(self, parent1, parent2):
partition = randint(0, len(self.population[0]) - 1)
return parent1[0:partition] + parent2[partition:]
def _mutate(self, member):
if self.mutation_rate >= random():
member = list(member)
member[randint(0,4)] = choice(lowercase)
member = ''.join(member)
return member
def test_algorithm():
algorithm = Algorithm(.5, .7, 500, max_fitness=None)
best_solution, best_objective_value = algorithm.run()To run tests, look in the tests folder.
Use pytest; it should automatically find the test files.
Feel free to send a pull request if you want to add any features or if you find a bug.
Check the issues tab for some potential things to do.
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